Signing Your Next Deal With Your Twitter @Username: The Legal Uses of Identity-Based Cryptography
Bibliographic record
Abstract
This article will look at the legal framework for electronic signatures under Canadian law and through the UNCITRAL Model Law on Electronic Signatures and evaluate the potential use of identity-based cryptography as a type of electronic signature. While most jurisdictions permit electronic signatures to replace their handwritten predecessors, the criteria of validity for an electronic signature range from liberal to restrictive. Public key infrastructure (PKI) cryptography schemes are considered to meet the juridical conditions of a legal signature under more rigorous legislation that requires an electronic signature to possess certain security attributes. In common law jurisdictions, digital signature schemes such as PKI have not been widely adopted in the private sector for use as secure electronic signatures. This may be due to the fact that they are difficult and awkward for the general public to use, rather than because of doubts surrounding certification authorities. This is not entirely the case in Europe and Latin America, where PKI digital signature schemes have been adopted by various governments programs. Case examples of PKI schemes include electronic identity cards issued by European governments such as Belgium’s eID. Though used by the government, the European private sector has widely neglected PKI electronic signature products. This is partly due to a lack of customer demand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".